class_docstr: Testing suite for Bark - tests all the tests defined in `BetterTransformersTestMixin`
  Since `Bark` is a text-to-speech model, it is preferrable
  to define its own testing class.
docstr: This tests if the converted model runs fine under fp16.
docstr_tok: ["This","tests","if","the","converted","model","runs","fine","under","fp16","."]
code: def _test_fp16_inference(
      self,
      model_id: str,
      model_type: str,
      automodel_class,
      use_to_operator=False,
      **preprocessor_kwargs,
  ):
      r"""
      This tests if the converted model runs fine under fp16.
      """
      # The first row of the attention mask needs to be all ones -> check: https://github.com/pytorch/pytorch/blob/19171a21ee8a9cc1a811ac46d3abd975f0b6fc3b/test/test_nn.py#L5283
      inputs = self.prepare_inputs_for_class(
          model_id=model_id,
          model_type=model_type,
          **preprocessor_kwargs,
      ).to(0)
  
      set_seed(0)
  
      if not use_to_operator:
          hf_random_model = automodel_class.from_pretrained(
              model_id, torch_dtype=torch.float16
          ).to(0)
          converted_model = BetterTransformer.transform(
              hf_random_model, keep_original_model=False
          )
  
          hf_random_model = automodel_class.from_pretrained(
              model_id, torch_dtype=torch.float16
          ).to(0)
      else:
          hf_random_model = automodel_class.from_pretrained(
              model_id
          ).to(0)
          converted_model = BetterTransformer.transform(
              hf_random_model, keep_original_model=False
          )
  
          hf_random_model = automodel_class.from_pretrained(
              model_id
          ).to(0)
          hf_random_model = hf_random_model.to(torch.float16)
          converted_model = converted_model.to(torch.float16)
  
      self.assertFalse(
          hasattr(hf_random_model, "use_bettertransformer"),
          f"The model {hf_random_model.__class__.__name__} has been converted to a `fast` model by mistake.",
      )
  
      length = 50
      rtol = 5e-2
  
      with torch.inference_mode():
          r"""
          Make sure the models are in eval mode! Make also sure that the original model
          has not been converted to a fast model. The check is done above.
          """
          output_hf = hf_random_model.generate(
              **inputs,
              fine_temperature=None,
              do_sample=False,
              semantic_max_new_tokens=length,
          )
  
          output_bt = converted_model.generate(
              **inputs,
              fine_temperature=None,
              do_sample=False,
              semantic_max_new_tokens=length,
          )
  
          self.assertTrue(
              (output_hf - output_bt).abs().mean() < rtol,
              f"Mean absolute diff: {(output_hf - output_bt).abs().mean()}",
          )
  
imports: import unittest
  import numpy
  import pytest
  import torch
  from parameterized import parameterized
  from testing_utils import MODELS_DICT, BetterTransformersTestMixin
  from transformers import AutoModel, AutoProcessor, set_seed
  from optimum.bettertransformer import BetterTransformer
  from optimum.utils.testing_utils import grid_parameters, require_torch_gpu
label_desc: Use image node_id 2 for calling the BetterTransformersBarkTest obj's underlying member method code with example usage: obj._test_fp16_inference(model_id, model_type, automodel_class, use_to_operator) without return types